Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system, comprising: one or more servers configured to: receive image data from a consumer device representing a receipt; determine a merchant identifier associated with the receipt based on logo detection; determine a receipt structure associated with the merchant identifier, wherein the receipt structure defines one or more receipt elements and one or more locations respectively associated with the one or more receipt elements; perform text extraction on the image data to generate receipt text data; associate the receipt text data with the one or more receipt elements of the receipt structure based on the one or more locations respectively associated with the one or more receipt elements to generate structured receipt data; for each receipt element of the structured receipt data: determine a format rule associated with the receipt element; and in an instance where the structured receipt data violates the format rule, update the structured receipt data associated with the receipt element based on the format rule to generate updated structured receipt data.
The system automates the processing of receipt images into structured digital data. It addresses the challenge of manually extracting and organizing receipt information, which is time-consuming and error-prone. The system receives an image of a receipt from a consumer device and analyzes it to identify the merchant using logo detection. Once the merchant is identified, the system retrieves a predefined receipt structure associated with that merchant, which defines the layout and locations of key elements such as item descriptions, prices, dates, and totals. The system then performs optical character recognition (OCR) to extract text from the receipt image and maps this text to the corresponding elements in the receipt structure, creating structured data. Each element in the structured data is validated against format rules, such as expected date formats or currency symbols. If discrepancies are found, the system corrects the data to ensure consistency and accuracy. The result is a standardized, machine-readable version of the receipt, which can be used for expense tracking, accounting, or other applications. The system improves efficiency by automating data extraction and validation, reducing manual effort and errors.
2. The system of claim 1 , wherein the one or more receipt elements include one or more of: one or more transaction receipt elements, one or more category receipt elements, or one or more item receipt elements.
This invention relates to a system for organizing and managing digital receipts, addressing the problem of fragmented and disorganized financial records in personal or business transactions. The system categorizes and stores receipt data in a structured manner to improve accessibility and analysis. The system includes one or more receipt elements, which are components that define the structure and content of the receipts. These receipt elements can include transaction receipt elements, which capture details of individual transactions such as date, amount, and payment method; category receipt elements, which group transactions by predefined categories like groceries, utilities, or entertainment; and item receipt elements, which break down transactions into individual items or line items, including descriptions, quantities, and prices. By incorporating these different types of receipt elements, the system enables users to track spending patterns, generate reports, and manage budgets more effectively. The structured organization of receipt data allows for seamless integration with financial management tools, enhancing financial transparency and decision-making. The system may also support automated data extraction from physical or digital receipts, further streamlining the process of record-keeping.
3. The system of claim 2 , wherein the one or more transaction receipt elements includes one or more of: a store location receipt element, a store contact info receipt element, a transaction date receipt element, or a transaction time receipt element.
This invention relates to a digital transaction receipt system designed to enhance customer experience and streamline transaction documentation. The system generates and manages digital receipts for retail transactions, incorporating various transaction details to provide comprehensive and accessible records. The system includes a transaction processing module that captures transaction data from point-of-sale systems or other retail platforms. A receipt generation module then formats this data into a digital receipt, which is stored in a receipt database for retrieval by customers. The system also features a user interface that allows customers to access their receipts via mobile devices or other digital platforms, enabling easy retrieval and management of transaction records. A key aspect of the system is the inclusion of multiple transaction receipt elements within the digital receipts. These elements may include a store location receipt element, which specifies the physical or virtual location where the transaction occurred; a store contact info receipt element, providing contact details for the store; a transaction date receipt element, indicating the date of the transaction; and a transaction time receipt element, specifying the time of the transaction. These elements ensure that the digital receipts are detailed and useful for customers, enabling them to reference transaction specifics when needed. The system may also include additional features such as receipt organization, search functionality, and integration with loyalty programs to enhance usability and customer engagement.
4. The system of claim 2 , wherein the one or more item receipt elements includes one or more of: a stock keeping unit identifier receipt element, an item type receipt element, an item brand receipt element, a unit size receipt element, a unit count receipt element, a per unit price receipt element, or a total item price receipt element.
This invention relates to a system for processing item receipt data, particularly in retail or inventory management contexts. The system addresses the challenge of accurately capturing and organizing detailed information about items during transactions or inventory checks. The core functionality involves generating and managing receipt elements that describe various attributes of items being processed. These elements include identifiers such as stock keeping unit (SKU) numbers, item types, brands, unit sizes, unit counts, per-unit prices, and total item prices. By incorporating these elements, the system ensures comprehensive tracking of item details, enabling better inventory management, pricing accuracy, and transaction verification. The system may be used in point-of-sale terminals, inventory management software, or automated checkout systems to enhance data precision and operational efficiency. The inclusion of multiple receipt elements allows for flexible configuration based on specific use cases, such as retail sales, wholesale inventory, or supply chain logistics. This detailed item data capture improves decision-making, reduces errors, and supports automated processes like reordering or pricing adjustments.
5. The system of claim 1 , wherein the receipt text data is machine data representing text extracted from the image data using machine optical text extraction techniques.
The invention relates to a system for processing receipt data, specifically focusing on extracting and utilizing text information from receipt images. The system addresses the challenge of manually entering or processing receipt data, which is time-consuming and prone to errors. By leveraging machine optical text extraction techniques, the system automatically converts image-based receipt data into machine-readable text, enabling efficient data processing and analysis. The system includes a component for capturing or receiving image data of receipts, which may be obtained through scanning, photography, or other imaging methods. The extracted text data is then processed to identify relevant information such as transaction details, dates, amounts, and vendor information. This extracted data can be used for various applications, including expense tracking, financial reporting, and automated accounting. The system may also include additional features such as data validation, formatting, and integration with other software systems to ensure accuracy and usability. By automating the extraction and processing of receipt text data, the system improves efficiency, reduces manual effort, and minimizes errors in receipt-related workflows. The use of machine optical text extraction techniques ensures that the system can handle diverse receipt formats and layouts, making it adaptable to various real-world scenarios.
6. The system of claim 1 , wherein the structured receipt data is defined based on a receipt structure language.
A system for processing and analyzing receipt data is disclosed, addressing the challenge of extracting and organizing information from unstructured or semi-structured receipts. The system captures receipt data from various sources, such as digital images or scanned documents, and converts it into a structured format for further analysis. This structured data includes key details like transaction amounts, dates, vendor information, and itemized purchases, enabling efficient storage, retrieval, and processing. The system includes a receipt structure language that defines the format and rules for organizing receipt data. This language ensures consistency in how data is extracted and stored, allowing for seamless integration with other systems, such as accounting or inventory management tools. The structured receipt data can be validated against predefined templates or schemas to ensure accuracy and completeness. Additionally, the system may include modules for enhancing the quality of the extracted data, such as optical character recognition (OCR) for improving text recognition from images, and machine learning algorithms for classifying and categorizing receipt items. The structured data can then be used for tasks like expense reporting, financial reconciliation, or automated auditing. By converting receipt data into a standardized format, the system simplifies data management and reduces manual effort in processing receipts, improving efficiency and accuracy in financial and operational workflows.
7. The system of claim 1 , wherein the text extraction is performed line by line.
A system for extracting text from images or documents processes the input by analyzing content line by line. The system includes a text extraction module that identifies and isolates individual lines of text within the input, ensuring accurate capture of each line's content. This line-by-line approach improves precision by reducing errors caused by overlapping or misaligned text regions. The system may also include preprocessing steps to enhance image quality, such as noise reduction or contrast adjustment, before text extraction. Additionally, the system may apply optical character recognition (OCR) to convert extracted text into machine-readable format. The line-by-line extraction method is particularly useful for structured documents, such as forms or tables, where maintaining the spatial relationship between lines is critical. The system may further include validation mechanisms to verify the accuracy of extracted text, such as comparing extracted lines against known patterns or dictionaries. This ensures high reliability in applications like document digitization, data entry automation, or archival systems. The system's modular design allows integration with other text processing tools, such as natural language processing (NLP) engines, for further analysis. The line-by-line extraction method enhances the system's adaptability to various document formats and improves overall text recognition performance.
8. The system of claim 1 , wherein the text extraction is performed receipt element by receipt element.
A system for processing receipts extracts text from receipts by analyzing individual receipt elements separately. The system first captures an image of a receipt and then identifies distinct elements within the receipt, such as item descriptions, prices, dates, and totals. Each element is processed individually to extract text, ensuring accurate recognition of each component. The extracted text is then organized into a structured format, such as a database or digital document, for further use. This approach improves accuracy by isolating and analyzing each element independently, reducing errors caused by overlapping or poorly formatted text. The system may also include preprocessing steps like image enhancement to improve text recognition quality. The extracted data can be used for expense tracking, accounting, or inventory management. The method ensures that even complex receipts with multiple items, discounts, or taxes are processed efficiently and accurately.
9. The system of claim 1 , wherein the format rule defines one or more receipt element constraints including one or more of: one or more alphabetical character sequences, one or more number sequences, one or more alphanumeric character sequences, or one or more symbol sequences.
This invention relates to a system for processing and validating receipt data, particularly focusing on enforcing format rules to ensure consistency and accuracy in receipt elements. The system addresses the challenge of handling diverse receipt formats, which often contain unstructured or inconsistent data, making automated processing difficult. The system includes a rule-based engine that applies predefined format rules to receipt elements, such as text, numbers, or symbols, to standardize and validate the data. The format rules define constraints for receipt elements, including alphabetical character sequences, number sequences, alphanumeric sequences, or symbol sequences. These constraints ensure that receipt data adheres to expected patterns, improving data reliability. For example, a rule may require a receipt element to contain only numeric characters for a price field or a specific symbol for a currency indicator. The system checks each receipt element against these rules, flagging or correcting deviations to maintain consistency. The system may also include a data extraction module that identifies and isolates receipt elements from raw receipt data, such as images or text files. The validation module then applies the format rules to these elements, ensuring they meet predefined criteria. This approach enhances data accuracy for downstream applications, such as financial analysis or inventory management. The system is adaptable to various receipt formats, making it suitable for diverse retail and transaction environments.
10. The system of claim 1 , wherein the one or more servers are further configured to track inventory levels of the merchant identifier based on the structured receipt data.
This invention relates to a system for managing merchant inventory using structured receipt data. The problem addressed is the lack of real-time inventory tracking for merchants, which can lead to stockouts, overstocking, or inaccurate sales data. The system includes one or more servers that process digital receipts from point-of-sale (POS) systems to extract structured data, such as product identifiers, quantities sold, and transaction timestamps. The servers then analyze this data to update inventory levels in real time. The system also associates each receipt with a merchant identifier to ensure accurate tracking for individual businesses. The servers further monitor inventory levels based on the structured receipt data, allowing merchants to maintain optimal stock levels and improve supply chain efficiency. The system may also integrate with external databases or APIs to enrich the receipt data with additional product details, such as supplier information or pricing trends. By automating inventory tracking through receipt data, the system reduces manual errors and provides merchants with actionable insights for better inventory management.
11. The system of claim 1 , wherein the one or more servers are further configured to update a consumer profile based on the structured receipt data by associating one or more of the one or more receipt elements with the consumer profile.
This invention relates to a system for processing and analyzing consumer purchase data from digital receipts. The system addresses the challenge of efficiently capturing and utilizing structured receipt data to enhance consumer profiles, enabling personalized recommendations, targeted marketing, and improved customer insights. The system includes one or more servers that receive and process digital receipts from consumers, extracting structured data elements such as product identifiers, purchase dates, quantities, and prices. These servers are configured to update a consumer profile by associating the extracted receipt elements with the profile, allowing for the aggregation of purchase history and behavior patterns. The system may also include a user interface for consumers to submit receipts, a database for storing receipt data, and analytics tools to derive insights from the collected information. By linking receipt elements to consumer profiles, the system enables businesses to track purchasing trends, identify preferences, and tailor offerings. The structured data allows for automated categorization and analysis, improving the accuracy of consumer profiling and enabling more effective marketing strategies. The system may also support integration with loyalty programs, payment platforms, and other retail systems to enhance data collection and personalization.
12. A method comprising: receiving image data from a consumer device representing a receipt; determining a merchant identifier associated with the receipt based on logo detection; determining a receipt structure associated with the merchant identifier, wherein the receipt structure defines one or more receipt elements and one or more locations respectively associated with the one or more receipt elements; performing text extraction on the image data to generate receipt text data; associating the receipt text data with the one or more receipt elements of the receipt structure based on the one or more locations respectively associated with the one or more receipt elements to generate structured receipt data; for each receipt element of the structured receipt data: determining a format rule associated with the receipt element; and in an instance where the structured receipt data violates the format rule, updating the structured receipt data associated with the receipt element based on the format rule to generate updated structured receipt data.
This invention relates to automated receipt processing systems that extract and structure data from digital receipt images. The problem addressed is the difficulty in accurately parsing unstructured receipt data into a standardized format for financial tracking, expense reporting, or other applications. The method involves receiving an image of a receipt from a consumer device, such as a smartphone. The system first identifies the merchant by detecting logos or other visual identifiers in the image. Based on the merchant, the system retrieves a predefined receipt structure that defines the expected layout and elements of the receipt, such as item descriptions, prices, dates, and totals. The system then performs optical character recognition (OCR) to extract text from the image. The extracted text is mapped to the predefined receipt elements based on their expected positions in the receipt structure, creating structured data. Each element is validated against format rules, such as expected date formats or numerical patterns. If discrepancies are found, the data is corrected according to the rules, ensuring consistency and accuracy in the final structured output. This approach improves automation in financial workflows by reducing manual data entry and improving data reliability.
13. The method of claim 12 , wherein the one or more receipt elements include one or more of: one or more transaction receipt elements, one or more category receipt elements, or one or more item receipt elements.
This invention relates to a system for organizing and managing digital receipts, particularly in a financial or transaction processing environment. The problem addressed is the difficulty of categorizing and retrieving transaction data from digital receipts, which often lack structured formatting or consistent metadata. The invention provides a method for processing digital receipts by extracting and classifying receipt elements into distinct categories to improve searchability and analysis. The method involves parsing a digital receipt to identify and extract one or more receipt elements. These elements are classified into at least one of three types: transaction receipt elements, category receipt elements, or item receipt elements. Transaction receipt elements include details such as transaction dates, amounts, and payment methods. Category receipt elements group transactions by predefined categories like "groceries" or "entertainment." Item receipt elements break down individual items within a transaction, including product names, quantities, and prices. By categorizing receipt elements in this way, the system enables more efficient data retrieval, financial tracking, and expense reporting. The method may also involve storing these classified elements in a structured database for further analysis or integration with accounting software. This approach enhances the usability of digital receipts by making transaction data more accessible and actionable.
14. The method of claim 13 , wherein the one or more transaction receipt elements includes one or more of: a store location receipt element, a store contact info receipt element, a transaction date receipt element, or a transaction time receipt element.
This invention relates to digital transaction receipts, specifically enhancing the information included in such receipts to improve user experience and functionality. The problem addressed is the lack of comprehensive and easily accessible transaction details in digital receipts, which can lead to inconvenience for users when they need to reference specific information about a purchase. The method involves generating a digital transaction receipt that includes one or more transaction receipt elements. These elements provide detailed information about the transaction, such as the store location where the purchase occurred, the store's contact information, the date of the transaction, and the time of the transaction. By including these elements, the digital receipt becomes a more useful tool for users, allowing them to quickly access relevant details without needing to search for additional documentation or contact the store for clarification. The inclusion of these elements ensures that users have all necessary information in one place, improving convenience and reducing the likelihood of errors or misunderstandings related to transaction details. This method can be applied to various types of transactions, including in-store purchases, online orders, and service-based transactions, making it widely applicable across different industries. The enhanced digital receipts can be stored electronically, shared with others, or printed as needed, providing flexibility in how users manage their transaction records.
15. The method of claim 13 , wherein the one or more item receipt elements includes one or more of: a stock keeping unit identifier receipt element, an item type receipt element, an item brand receipt element, a unit size receipt element, a unit count receipt element, a per unit price receipt element, or a total item price receipt element.
This invention relates to systems and methods for generating and processing item receipt elements in a retail or transactional environment. The problem addressed is the need for detailed, structured receipt information to improve inventory management, pricing accuracy, and customer transparency. The invention provides a method for creating and handling receipt data that includes various item-specific details to enhance record-keeping and analysis. The method involves generating receipt elements for items involved in a transaction, where each element contains specific attributes related to the item. These attributes may include a stock keeping unit (SKU) identifier, item type, brand, unit size, unit count, per-unit price, and total item price. By capturing these details, the system enables precise tracking of inventory, pricing consistency, and improved data analytics for businesses. The receipt elements can be used for auditing, reporting, or customer reference, ensuring that all relevant transaction details are documented in a structured format. This approach enhances operational efficiency by reducing errors in pricing and inventory management while providing a comprehensive record of transactions.
16. The method of claim 12 , wherein the receipt text data is machine data representing text extracted from the image data using machine optical text extraction techniques.
A system and method for processing receipt data involves extracting text from image data of receipts using machine-based optical text extraction techniques. The extracted text data is then analyzed to identify relevant information such as transaction details, merchant information, and payment amounts. The system may further validate the extracted data by cross-referencing it with known merchant databases or transaction records. The method ensures accurate and automated extraction of receipt information, reducing manual data entry errors and improving efficiency in financial record-keeping. The system may also support multiple receipt formats and languages, enhancing its applicability across different regions and industries. The extracted data can be stored in a structured format for further processing, such as expense reporting, accounting, or auditing. The use of machine-based optical text extraction ensures high accuracy and speed in processing receipts, making it suitable for both personal and enterprise-level applications. The system may also include error correction mechanisms to handle low-quality or partially obscured receipt images, ensuring reliable data extraction even under suboptimal conditions.
17. The method of claim 12 , wherein the structured receipt data is defined based on a receipt structure language.
A system and method for processing and analyzing receipt data involves extracting and structuring information from receipts to enable automated analysis and integration with financial or inventory systems. The method addresses the challenge of manually entering receipt data, which is time-consuming and prone to errors. The system captures receipt images or digital receipts, then applies optical character recognition (OCR) or parsing techniques to extract relevant data such as item descriptions, quantities, prices, and totals. The extracted data is then structured into a standardized format, allowing for seamless integration with accounting software, expense tracking systems, or inventory management tools. This structured data can be used for expense reporting, tax deductions, or inventory reconciliation. The structured receipt data is defined using a receipt structure language, which provides a standardized way to represent receipt information, ensuring consistency and compatibility across different systems. The method may also include validating the extracted data against known product databases or merchant records to improve accuracy. By automating the receipt processing workflow, the system reduces manual effort, minimizes errors, and enhances data usability for financial and operational analysis.
18. The method of claim 12 , wherein the text extraction is performed line by line.
A system and method for extracting text from images or documents involves processing the content line by line to improve accuracy and efficiency. The method includes capturing an image of a document or other text-containing surface, preprocessing the image to enhance readability, and then extracting text line by line. This line-by-line approach ensures that each segment of text is individually analyzed, reducing errors caused by overlapping or distorted characters. The extracted text is then stored or transmitted for further processing, such as optical character recognition (OCR), data analysis, or archival purposes. The system may also include features for correcting orientation, adjusting contrast, and removing noise to improve extraction quality. This method is particularly useful in applications where precise text retrieval is critical, such as document digitization, automated data entry, and archival systems. The line-by-line extraction technique enhances reliability by isolating and processing each line independently, minimizing the impact of formatting inconsistencies or background interference. The system may also incorporate machine learning models to refine text recognition over time, adapting to different fonts, languages, and document layouts.
19. The method of claim 12 , wherein the format rule defines one or more receipt element constraints including one or more of: one or more alphabetical character sequences, one or more number sequences, one or more alphanumeric character sequences, or one or more symbol sequences.
This invention relates to systems and methods for processing and validating receipt data, particularly in automated transaction systems. The problem addressed is the need to accurately extract and validate structured information from receipts, such as transaction details, dates, or amounts, to ensure data integrity in financial or inventory management applications. The method involves analyzing a digital image or scanned copy of a receipt to identify and extract relevant elements based on predefined format rules. These rules define constraints for receipt elements, including sequences of alphabetical characters, numerical values, alphanumeric combinations, or symbols. The system applies these constraints to validate the extracted data, ensuring it matches expected patterns (e.g., a date format like MM/DD/YYYY or a currency value with a dollar sign). The method may also involve comparing extracted data against known templates or databases to confirm accuracy. The invention improves upon prior art by providing flexible, rule-based validation that adapts to different receipt formats, reducing errors in automated data processing. This is particularly useful in retail, banking, or logistics, where receipt data must be reliably captured for record-keeping or analysis. The system may integrate with optical character recognition (OCR) tools to enhance extraction accuracy.
20. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instruction stored therein, the computer-executable program code instructions comprising program code instructions configured to: receive image data from a consumer device representing a receipt; determine a merchant identifier associated with the receipt based on logo detection; determine a receipt structure associated with the merchant identifier, wherein the receipt structure defines one or more receipt elements and one or more locations respectively associated with the one or more receipt elements; perform text extraction on the image data to generate receipt text data; associate the receipt text data with the one or more receipt elements of the receipt structure based on the one or more locations respectively associated with the one or more receipt elements to generate structured receipt data; for each receipt element of the structured receipt data: determine a format rule associated with the receipt element; and in an instance where the structured receipt data violates the format rule, update the structured receipt data associated with the receipt element based on the format rule to generate updated structured receipt data.
This invention relates to automated receipt processing systems that extract and structure data from digital images of receipts. The technology addresses the challenge of accurately parsing unstructured receipt images into machine-readable formats, which is difficult due to variations in merchant layouts, fonts, and formatting. The system receives an image of a receipt from a consumer device and analyzes it to identify the merchant using logo detection. Once the merchant is identified, the system retrieves a predefined receipt structure associated with that merchant, which defines the expected elements (e.g., date, total amount, items) and their locations on the receipt. The system then performs optical character recognition (OCR) to extract text from the image and maps the extracted text to the corresponding elements in the receipt structure based on their predefined locations, generating structured receipt data. Each element in the structured data is validated against format rules (e.g., date formats, currency symbols). If discrepancies are found, the data is corrected according to the rules, producing a standardized, error-free output. This approach improves accuracy in receipt data extraction and ensures consistency for further processing, such as expense tracking or financial analysis.
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January 12, 2021
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